28th November 2017
Some background
25,000 genes
200 compatibility genes
6 class I HLA genes
Who, what and when
1936 Peter Gorer
1940s and WWII
1958 HLA gene identified
Thousands have been idenitified to date
1986 Alain Townsend
What HLA proteins are showing the immune system
1987 Pamela Bjorkman
The structure of the HLA protein
1990 to the present
The peptide loading complex, peptide trimmers etc.
Recap
The known knowns
- There are thousands of these HLA genes
- We each have up to six
- HLA proteins present peptides
- And that's how CD8+ T-cells know what is going inside cells
Completing the Rumsfeld triplet
- We don't know the mechanism of peptide generation
- We don't know the mechanism of peptide selection by HLA
- We don't know what T-cells are seeing at any given time
- We don't know what we don't know
ImmunoSense Overview
Objectives
- Characterise the HaCaT immunopeptidome \(\pm\)DNCB.
- Characterise HaCaT protein turnover/abundance \(\pm\)DNCB.
- Infer relationship(s) between DNCB, peptidome, protein turn over and protein abundance.
- Identify (and test) candidate immunogenic peptides.
Method development
Capturing MHC I peptides | Observing protein turnover
Peptidome
MHC I motifs
Peptidome results 1
Database identified : STTGH(C)VHMR
De novo identified : YMDVEE(K)LLF
Protein turnover
Protein turnover
Protein turnover
Protein turnover
Protein turnover
Protein turnover
Protein turnover
Experimental next steps
Complete HaCaT dataset | Start HLA-A2 transfectant dataset
Method development
Quantifying our uncertainty
Peptidome areas of uncertainty
- How many peptide species are there per treatment?
- Is the size of the peptidome repertoire different between treatments?
- Can we estimate the probability that a peptide is unique to one treatment?
Paul A. Smith, S3RI University of Southampton
Protein turnover
Binary general linear model
dat_turnover[c(1:5,902:907),1:3]
## # A tibble: 11 x 3 ## Peptide Treatment Turnover.Rate ## <int> <chr> <dbl> ## 1 1 Control 0.149 ## 2 0 Control 0.513 ## 3 0 Control 0.491 ## 4 1 Control 0.614 ## 5 1 Control 0.445 ## 6 0 DNCB 0.111 ## 7 1 DNCB 0.794 ## 8 1 DNCB 0.704 ## 9 0 DNCB 0.268 ## 10 0 DNCB 0.393 ## 11 1 DNCB 0.802
Protein turnover
Binary general linear model
\(Y_i = \beta_0 + \beta_1.X_i + \epsilon_i\)
\(X_i\) : protein turnover rate for protein \(X\).
\(Y_i\) : probability of observing peptide from protein \(X\) at the cell surface.
\(\beta_0\) : expected value of \(Y_i\) under given treatment.
\(\beta_1\) : increase in probability of \(Y_i\) due to increase in turnover rate.
\(\epsilon_i\) : is the residual error term.
Models for \(\beta_0\) and \(\beta_1\)